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Advanced chatbots with deep learning & Python
Advanced chatbots with deep learning and Python
Chatbots development with deep learning
Packt Publishing, publisher. ; AI Sciences (Firm), presenter. ; Hamid, Shahzaib, speaker, instructor.

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ICEPY4D: A PYTHON TOOLKIT FOR ADVANCED MULTI-EPOCH GLACIER MONITORING WITH DEEP-LEARNING PHOTOGRAMMETRY
Ioli, F. ; Barbieri, F. ; Gaspari, F. ; et al.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-1-W2-2023, Pp 1037-1044 (2023)

Technology 13. Climate action Time-lapse cameras, Wide... 0211 other engineering a... Applied optics. Photonic... 02 engineering and techn... …
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1. American Heart Association. (2021). Heart disease and stroke statistics—2021 update. Circulation, 143(8), e254-e743. 2. Rahman, M., Al Amin, M., Hasan, R., Hossain, S. T., Rahman, M. H., & Rashed, R. A. M. (2025). A Predictive AI Framework for Cardiovascular Disease Screening in the US: Integrating EHR Data with Machine and Deep Learning Models. British Journal of Nursing Studies, 5(2), 40-48. 3. ZakirHossain, M., Khan, M. M., Thapa, S., Uddin, R., Meem, E. J., Niloy, S. K., ... & Bhavani, G. D. (2025, February). Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases. In 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) (pp. 1-6). IEEE. 4. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785-794). ACM. 5. Damen, J. A., Hooft, L., Schuit, E., Debray, T. P., Collins, G. S., Tzoulaki, I., Lassale, C. M., Siontis, G. C., Chiocchia, V., Roberts, C., Schlüssel, M. M., Gerry, S., Black, J. A., Heus, P., van der Schouw, Y. T., Peelen, L. M., & Moons, K. G. (2016). Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ, 353, i2416. 6. Framingham Heart Study. (1948). Framingham Heart Study cohort research data. National Heart, Lung, and Blood Institute. 7. Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035. 8. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657-2664. 9. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (NIPS 2017) (pp. 4765-4774). 10. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. 11. Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018). Machine learning in cardiovascular medicine: are we there yet? Heart, 104(14), 1156-1164. 12. Steyerberg, E. W., Vergouwe, Y., & van Calster, B. (2019). Towards better clinical prediction models: seven steps for development and an ABCD for validation. European Heart Journal, 40(15), 1255–1264. 13. Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A., Sprosen, T., Peakman, T., & Collins, R. (2015). UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Medicine, 12(3), e1001779. 14. Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS ONE, 12(4), e0174944. 15. World Health Organization. (2021). Cardiovascular diseases (CVDs). Retrieved from https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) 16. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., ... Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265–283). 17. Chollet, F. (2015). Keras (Version 2.4.0) [Computer software]. https://github.com/fchollet/keras
Okunola, Abiodun

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Leveraging advanced characterisation of the derivatives of pre-processed coal fly ash using deep learning and digital image processing techniques
Kanesalingam, Brinthan ; Fernando, W. Ashane M. ; Jayawardena, Chulantha ; et al.
In Materials Today Communications July 2025 47

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An advanced deep learning approach for energy absorption prediction in porous metals across diverse strain rate scenarios
Tang, Minghai ; Wang, Lei ; Song, Junyong
In Computational Materials Science May 2025 253

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11

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python
Liang, Xuechen ; He, Xiaoxing ; Wang, Shengdao ; et al.

Machine Learning
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13

Novel Deep Learning Model for Predicting Wind Velocity and Power Estimation in Advanced INVELOX Wind Turbines
K. Ramesh Kumar ; M. Selvaraj
Journal of Applied Fluid Mechanics, Vol 16, Iss 6, Pp 1256-1268 (2023)

deep learning advanced invelox wind tu... long short-term memory black widow optimization mayfly optimization python …
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Accurate Lung Cancer Prediction From CT Scans Using Advanced Deep Learning Methods.
Sharma A ; Kandoi NM
Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 8207754 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1537-453X (Electronic) Linking ISSN: 02773732 NLM ISO Abbreviation: Am J Clin Oncol Subsets: MEDLINE

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15

Advanced Glacier Monitoring with ICEpy4D: A Python Toolkit for Multi-Epoch Analysis using Deep-Learning Photogrammetry
Helmholtz-Institut Freiberg für Ressourcentechnologie ; Technische Universität Dresden ; Ioli, F. ; et al.

Electronic Resource
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16

Advanced Glacier Monitoring with ICEpy4D : A Python Toolkit for Multi-Epoch Analysis using Deep-Learning Photogrammetry
Ioli, F. ; Nex, F. ; Pinto, L. ; et al.
VGC 2023 - Unveiling the dynamic Earth with digital methods : 5th Virtual Geoscience Conference ; Book of Abstracts. :42-43

ddc:550 Time-lapse cameras, low-...
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17

SAIPy: A Python package for single-station earthquake monitoring using deep learning
Li, Wei ; Chakraborty, Megha ; Cartaya, Claudia Quinteros ; et al.
In Computers and Geosciences October 2024 192

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18

DeepInverse: A Python package for solving imaging inverse problems with deep learning
Tachella, Julián ; Terris, Matthieu ; Hurault, Samuel ; et al.
Journal of Open Source Software. 10(115):8923-8923

[INFO.INFO-TI]Computer S... Image Processing [eess.I...
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19

Deep Learning and Machine Learning -- Python Data Structures and Mathematics Fundamental: From Theory to Practice
Chen, Silin ; Bi, Ziqian ; Liu, Junyu ; et al.

Machine Learning Data Structures and Algo... Programming Languages
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